// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_CHOLMODSUPPORT_H
#define EIGEN_CHOLMODSUPPORT_H

namespace Eigen {

namespace internal {

    template <typename Scalar> struct cholmod_configure_matrix;

    template <> struct cholmod_configure_matrix<double>
    {
        template <typename CholmodType> static void run(CholmodType& mat)
        {
            mat.xtype = CHOLMOD_REAL;
            mat.dtype = CHOLMOD_DOUBLE;
        }
    };

    template <> struct cholmod_configure_matrix<std::complex<double>>
    {
        template <typename CholmodType> static void run(CholmodType& mat)
        {
            mat.xtype = CHOLMOD_COMPLEX;
            mat.dtype = CHOLMOD_DOUBLE;
        }
    };

    // Other scalar types are not yet supported by Cholmod
    // template<> struct cholmod_configure_matrix<float> {
    //   template<typename CholmodType>
    //   static void run(CholmodType& mat) {
    //     mat.xtype = CHOLMOD_REAL;
    //     mat.dtype = CHOLMOD_SINGLE;
    //   }
    // };
    //
    // template<> struct cholmod_configure_matrix<std::complex<float> > {
    //   template<typename CholmodType>
    //   static void run(CholmodType& mat) {
    //     mat.xtype = CHOLMOD_COMPLEX;
    //     mat.dtype = CHOLMOD_SINGLE;
    //   }
    // };

}  // namespace internal

/** Wraps the Eigen sparse matrix \a mat into a Cholmod sparse matrix object.
  * Note that the data are shared.
  */
template <typename _Scalar, int _Options, typename _StorageIndex> cholmod_sparse viewAsCholmod(Ref<SparseMatrix<_Scalar, _Options, _StorageIndex>> mat)
{
    cholmod_sparse res;
    res.nzmax = mat.nonZeros();
    res.nrow = mat.rows();
    res.ncol = mat.cols();
    res.p = mat.outerIndexPtr();
    res.i = mat.innerIndexPtr();
    res.x = mat.valuePtr();
    res.z = 0;
    res.sorted = 1;
    if (mat.isCompressed())
    {
        res.packed = 1;
        res.nz = 0;
    }
    else
    {
        res.packed = 0;
        res.nz = mat.innerNonZeroPtr();
    }

    res.dtype = 0;
    res.stype = -1;

    if (internal::is_same<_StorageIndex, int>::value)
    {
        res.itype = CHOLMOD_INT;
    }
    else if (internal::is_same<_StorageIndex, SuiteSparse_long>::value)
    {
        res.itype = CHOLMOD_LONG;
    }
    else
    {
        eigen_assert(false && "Index type not supported yet");
    }

    // setup res.xtype
    internal::cholmod_configure_matrix<_Scalar>::run(res);

    res.stype = 0;

    return res;
}

template <typename _Scalar, int _Options, typename _Index> const cholmod_sparse viewAsCholmod(const SparseMatrix<_Scalar, _Options, _Index>& mat)
{
    cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar, _Options, _Index>>(mat.const_cast_derived()));
    return res;
}

template <typename _Scalar, int _Options, typename _Index> const cholmod_sparse viewAsCholmod(const SparseVector<_Scalar, _Options, _Index>& mat)
{
    cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar, _Options, _Index>>(mat.const_cast_derived()));
    return res;
}

/** Returns a view of the Eigen sparse matrix \a mat as Cholmod sparse matrix.
  * The data are not copied but shared. */
template <typename _Scalar, int _Options, typename _Index, unsigned int UpLo>
cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<const SparseMatrix<_Scalar, _Options, _Index>, UpLo>& mat)
{
    cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar, _Options, _Index>>(mat.matrix().const_cast_derived()));

    if (UpLo == Upper)
        res.stype = 1;
    if (UpLo == Lower)
        res.stype = -1;
    // swap stype for rowmajor matrices (only works for real matrices)
    EIGEN_STATIC_ASSERT((_Options & RowMajorBit) == 0 || NumTraits<_Scalar>::IsComplex == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
    if (_Options & RowMajorBit)
        res.stype *= -1;

    return res;
}

/** Returns a view of the Eigen \b dense matrix \a mat as Cholmod dense matrix.
  * The data are not copied but shared. */
template <typename Derived> cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat)
{
    EIGEN_STATIC_ASSERT((internal::traits<Derived>::Flags & RowMajorBit) == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
    typedef typename Derived::Scalar Scalar;

    cholmod_dense res;
    res.nrow = mat.rows();
    res.ncol = mat.cols();
    res.nzmax = res.nrow * res.ncol;
    res.d = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride();
    res.x = (void*)(mat.derived().data());
    res.z = 0;

    internal::cholmod_configure_matrix<Scalar>::run(res);

    return res;
}

/** Returns a view of the Cholmod sparse matrix \a cm as an Eigen sparse matrix.
  * The data are not copied but shared. */
template <typename Scalar, int Flags, typename StorageIndex> MappedSparseMatrix<Scalar, Flags, StorageIndex> viewAsEigen(cholmod_sparse& cm)
{
    return MappedSparseMatrix<Scalar, Flags, StorageIndex>(cm.nrow,
                                                           cm.ncol,
                                                           static_cast<StorageIndex*>(cm.p)[cm.ncol],
                                                           static_cast<StorageIndex*>(cm.p),
                                                           static_cast<StorageIndex*>(cm.i),
                                                           static_cast<Scalar*>(cm.x));
}

namespace internal {

    // template specializations for int and long that call the correct cholmod method

#define EIGEN_CHOLMOD_SPECIALIZE0(ret, name)                                                                           \
    template <typename _StorageIndex> inline ret cm_##name(cholmod_common& Common) { return cholmod_##name(&Common); } \
    template <> inline ret cm_##name<SuiteSparse_long>(cholmod_common & Common) { return cholmod_l_##name(&Common); }

#define EIGEN_CHOLMOD_SPECIALIZE1(ret, name, t1, a1)                                                                                \
    template <typename _StorageIndex> inline ret cm_##name(t1& a1, cholmod_common& Common) { return cholmod_##name(&a1, &Common); } \
    template <> inline ret cm_##name<SuiteSparse_long>(t1 & a1, cholmod_common & Common) { return cholmod_l_##name(&a1, &Common); }

    EIGEN_CHOLMOD_SPECIALIZE0(int, start)
    EIGEN_CHOLMOD_SPECIALIZE0(int, finish)

    EIGEN_CHOLMOD_SPECIALIZE1(int, free_factor, cholmod_factor*, L)
    EIGEN_CHOLMOD_SPECIALIZE1(int, free_dense, cholmod_dense*, X)
    EIGEN_CHOLMOD_SPECIALIZE1(int, free_sparse, cholmod_sparse*, A)

    EIGEN_CHOLMOD_SPECIALIZE1(cholmod_factor*, analyze, cholmod_sparse, A)

    template <typename _StorageIndex> inline cholmod_dense* cm_solve(int sys, cholmod_factor& L, cholmod_dense& B, cholmod_common& Common)
    {
        return cholmod_solve(sys, &L, &B, &Common);
    }
    template <> inline cholmod_dense* cm_solve<SuiteSparse_long>(int sys, cholmod_factor& L, cholmod_dense& B, cholmod_common& Common)
    {
        return cholmod_l_solve(sys, &L, &B, &Common);
    }

    template <typename _StorageIndex> inline cholmod_sparse* cm_spsolve(int sys, cholmod_factor& L, cholmod_sparse& B, cholmod_common& Common)
    {
        return cholmod_spsolve(sys, &L, &B, &Common);
    }
    template <> inline cholmod_sparse* cm_spsolve<SuiteSparse_long>(int sys, cholmod_factor& L, cholmod_sparse& B, cholmod_common& Common)
    {
        return cholmod_l_spsolve(sys, &L, &B, &Common);
    }

    template <typename _StorageIndex>
    inline int cm_factorize_p(cholmod_sparse* A, double beta[2], _StorageIndex* fset, std::size_t fsize, cholmod_factor* L, cholmod_common& Common)
    {
        return cholmod_factorize_p(A, beta, fset, fsize, L, &Common);
    }
    template <>
    inline int
    cm_factorize_p<SuiteSparse_long>(cholmod_sparse* A, double beta[2], SuiteSparse_long* fset, std::size_t fsize, cholmod_factor* L, cholmod_common& Common)
    {
        return cholmod_l_factorize_p(A, beta, fset, fsize, L, &Common);
    }

#undef EIGEN_CHOLMOD_SPECIALIZE0
#undef EIGEN_CHOLMOD_SPECIALIZE1

}  // namespace internal

enum CholmodMode
{
    CholmodAuto,
    CholmodSimplicialLLt,
    CholmodSupernodalLLt,
    CholmodLDLt
};

/** \ingroup CholmodSupport_Module
  * \class CholmodBase
  * \brief The base class for the direct Cholesky factorization of Cholmod
  * \sa class CholmodSupernodalLLT, class CholmodSimplicialLDLT, class CholmodSimplicialLLT
  */
template <typename _MatrixType, int _UpLo, typename Derived> class CholmodBase : public SparseSolverBase<Derived>
{
protected:
    typedef SparseSolverBase<Derived> Base;
    using Base::derived;
    using Base::m_isInitialized;

public:
    typedef _MatrixType MatrixType;
    enum
    {
        UpLo = _UpLo
    };
    typedef typename MatrixType::Scalar Scalar;
    typedef typename MatrixType::RealScalar RealScalar;
    typedef MatrixType CholMatrixType;
    typedef typename MatrixType::StorageIndex StorageIndex;
    enum
    {
        ColsAtCompileTime = MatrixType::ColsAtCompileTime,
        MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
    };

public:
    CholmodBase() : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false)
    {
        EIGEN_STATIC_ASSERT((internal::is_same<double, RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
        m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
        internal::cm_start<StorageIndex>(m_cholmod);
    }

    explicit CholmodBase(const MatrixType& matrix) : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false)
    {
        EIGEN_STATIC_ASSERT((internal::is_same<double, RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
        m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
        internal::cm_start<StorageIndex>(m_cholmod);
        compute(matrix);
    }

    ~CholmodBase()
    {
        if (m_cholmodFactor)
            internal::cm_free_factor<StorageIndex>(m_cholmodFactor, m_cholmod);
        internal::cm_finish<StorageIndex>(m_cholmod);
    }

    inline StorageIndex cols() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
    inline StorageIndex rows() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }

    /** \brief Reports whether previous computation was successful.
      *
      * \returns \c Success if computation was successful,
      *          \c NumericalIssue if the matrix.appears to be negative.
      */
    ComputationInfo info() const
    {
        eigen_assert(m_isInitialized && "Decomposition is not initialized.");
        return m_info;
    }

    /** Computes the sparse Cholesky decomposition of \a matrix */
    Derived& compute(const MatrixType& matrix)
    {
        analyzePattern(matrix);
        factorize(matrix);
        return derived();
    }

    /** Performs a symbolic decomposition on the sparsity pattern of \a matrix.
      *
      * This function is particularly useful when solving for several problems having the same structure.
      *
      * \sa factorize()
      */
    void analyzePattern(const MatrixType& matrix)
    {
        if (m_cholmodFactor)
        {
            internal::cm_free_factor<StorageIndex>(m_cholmodFactor, m_cholmod);
            m_cholmodFactor = 0;
        }
        cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
        m_cholmodFactor = internal::cm_analyze<StorageIndex>(A, m_cholmod);

        this->m_isInitialized = true;
        this->m_info = Success;
        m_analysisIsOk = true;
        m_factorizationIsOk = false;
    }

    /** Performs a numeric decomposition of \a matrix
      *
      * The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been performed.
      *
      * \sa analyzePattern()
      */
    void factorize(const MatrixType& matrix)
    {
        eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
        cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
        internal::cm_factorize_p<StorageIndex>(&A, m_shiftOffset, 0, 0, m_cholmodFactor, m_cholmod);

        // If the factorization failed, minor is the column at which it did. On success minor == n.
        this->m_info = (m_cholmodFactor->minor == m_cholmodFactor->n ? Success : NumericalIssue);
        m_factorizationIsOk = true;
    }

    /** Returns a reference to the Cholmod's configuration structure to get a full control over the performed operations.
     *  See the Cholmod user guide for details. */
    cholmod_common& cholmod() { return m_cholmod; }

#ifndef EIGEN_PARSED_BY_DOXYGEN
    /** \internal */
    template <typename Rhs, typename Dest> void _solve_impl(const MatrixBase<Rhs>& b, MatrixBase<Dest>& dest) const
    {
        eigen_assert(m_factorizationIsOk &&
                     "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
        const Index size = m_cholmodFactor->n;
        EIGEN_UNUSED_VARIABLE(size);
        eigen_assert(size == b.rows());

        // Cholmod needs column-major storage without inner-stride, which corresponds to the default behavior of Ref.
        Ref<const Matrix<typename Rhs::Scalar, Dynamic, Dynamic, ColMajor>> b_ref(b.derived());

        cholmod_dense b_cd = viewAsCholmod(b_ref);
        cholmod_dense* x_cd = internal::cm_solve<StorageIndex>(CHOLMOD_A, *m_cholmodFactor, b_cd, m_cholmod);
        if (!x_cd)
        {
            this->m_info = NumericalIssue;
            return;
        }
        // TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
        // NOTE Actually, the copy can be avoided by calling cholmod_solve2 instead of cholmod_solve
        dest = Matrix<Scalar, Dest::RowsAtCompileTime, Dest::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x), b.rows(), b.cols());
        internal::cm_free_dense<StorageIndex>(x_cd, m_cholmod);
    }

    /** \internal */
    template <typename RhsDerived, typename DestDerived> void _solve_impl(const SparseMatrixBase<RhsDerived>& b, SparseMatrixBase<DestDerived>& dest) const
    {
        eigen_assert(m_factorizationIsOk &&
                     "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
        const Index size = m_cholmodFactor->n;
        EIGEN_UNUSED_VARIABLE(size);
        eigen_assert(size == b.rows());

        // note: cs stands for Cholmod Sparse
        Ref<SparseMatrix<typename RhsDerived::Scalar, ColMajor, typename RhsDerived::StorageIndex>> b_ref(b.const_cast_derived());
        cholmod_sparse b_cs = viewAsCholmod(b_ref);
        cholmod_sparse* x_cs = internal::cm_spsolve<StorageIndex>(CHOLMOD_A, *m_cholmodFactor, b_cs, m_cholmod);
        if (!x_cs)
        {
            this->m_info = NumericalIssue;
            return;
        }
        // TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
        // NOTE cholmod_spsolve in fact just calls the dense solver for blocks of 4 columns at a time (similar to Eigen's sparse solver)
        dest.derived() = viewAsEigen<typename DestDerived::Scalar, ColMajor, typename DestDerived::StorageIndex>(*x_cs);
        internal::cm_free_sparse<StorageIndex>(x_cs, m_cholmod);
    }
#endif  // EIGEN_PARSED_BY_DOXYGEN

    /** Sets the shift parameter that will be used to adjust the diagonal coefficients during the numerical factorization.
      *
      * During the numerical factorization, an offset term is added to the diagonal coefficients:\n
      * \c d_ii = \a offset + \c d_ii
      *
      * The default is \a offset=0.
      *
      * \returns a reference to \c *this.
      */
    Derived& setShift(const RealScalar& offset)
    {
        m_shiftOffset[0] = double(offset);
        return derived();
    }

    /** \returns the determinant of the underlying matrix from the current factorization */
    Scalar determinant() const
    {
        using std::exp;
        return exp(logDeterminant());
    }

    /** \returns the log determinant of the underlying matrix from the current factorization */
    Scalar logDeterminant() const
    {
        using numext::real;
        using std::log;
        eigen_assert(m_factorizationIsOk &&
                     "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");

        RealScalar logDet = 0;
        Scalar* x = static_cast<Scalar*>(m_cholmodFactor->x);
        if (m_cholmodFactor->is_super)
        {
            // Supernodal factorization stored as a packed list of dense column-major blocs,
            // as described by the following structure:

            // super[k] == index of the first column of the j-th super node
            StorageIndex* super = static_cast<StorageIndex*>(m_cholmodFactor->super);
            // pi[k] == offset to the description of row indices
            StorageIndex* pi = static_cast<StorageIndex*>(m_cholmodFactor->pi);
            // px[k] == offset to the respective dense block
            StorageIndex* px = static_cast<StorageIndex*>(m_cholmodFactor->px);

            Index nb_super_nodes = m_cholmodFactor->nsuper;
            for (Index k = 0; k < nb_super_nodes; ++k)
            {
                StorageIndex ncols = super[k + 1] - super[k];
                StorageIndex nrows = pi[k + 1] - pi[k];

                Map<const Array<Scalar, 1, Dynamic>, 0, InnerStride<>> sk(x + px[k], ncols, InnerStride<>(nrows + 1));
                logDet += sk.real().log().sum();
            }
        }
        else
        {
            // Simplicial factorization stored as standard CSC matrix.
            StorageIndex* p = static_cast<StorageIndex*>(m_cholmodFactor->p);
            Index size = m_cholmodFactor->n;
            for (Index k = 0; k < size; ++k) logDet += log(real(x[p[k]]));
        }
        if (m_cholmodFactor->is_ll)
            logDet *= 2.0;
        return logDet;
    };

    template <typename Stream> void dumpMemory(Stream& /*s*/) {}

protected:
    mutable cholmod_common m_cholmod;
    cholmod_factor* m_cholmodFactor;
    double m_shiftOffset[2];
    mutable ComputationInfo m_info;
    int m_factorizationIsOk;
    int m_analysisIsOk;
};

/** \ingroup CholmodSupport_Module
  * \class CholmodSimplicialLLT
  * \brief A simplicial direct Cholesky (LLT) factorization and solver based on Cholmod
  *
  * This class allows to solve for A.X = B sparse linear problems via a simplicial LL^T Cholesky factorization
  * using the Cholmod library.
  * This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Therefore, it has little practical interest.
  * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
  * X and B can be either dense or sparse.
  *
  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
  *               or Upper. Default is Lower.
  *
  * \implsparsesolverconcept
  *
  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
  *
  * \warning Only double precision real and complex scalar types are supported by Cholmod.
  *
  * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLLT
  */
template <typename _MatrixType, int _UpLo = Lower> class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT<_MatrixType, _UpLo>>
{
    typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT> Base;
    using Base::m_cholmod;

public:
    typedef _MatrixType MatrixType;

    CholmodSimplicialLLT() : Base() { init(); }

    CholmodSimplicialLLT(const MatrixType& matrix) : Base()
    {
        init();
        this->compute(matrix);
    }

    ~CholmodSimplicialLLT() {}

protected:
    void init()
    {
        m_cholmod.final_asis = 0;
        m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
        m_cholmod.final_ll = 1;
    }
};

/** \ingroup CholmodSupport_Module
  * \class CholmodSimplicialLDLT
  * \brief A simplicial direct Cholesky (LDLT) factorization and solver based on Cholmod
  *
  * This class allows to solve for A.X = B sparse linear problems via a simplicial LDL^T Cholesky factorization
  * using the Cholmod library.
  * This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Therefore, it has little practical interest.
  * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
  * X and B can be either dense or sparse.
  *
  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
  *               or Upper. Default is Lower.
  *
  * \implsparsesolverconcept
  *
  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
  *
  * \warning Only double precision real and complex scalar types are supported by Cholmod.
  *
  * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLDLT
  */
template <typename _MatrixType, int _UpLo = Lower>
class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT<_MatrixType, _UpLo>>
{
    typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT> Base;
    using Base::m_cholmod;

public:
    typedef _MatrixType MatrixType;

    CholmodSimplicialLDLT() : Base() { init(); }

    CholmodSimplicialLDLT(const MatrixType& matrix) : Base()
    {
        init();
        this->compute(matrix);
    }

    ~CholmodSimplicialLDLT() {}

protected:
    void init()
    {
        m_cholmod.final_asis = 1;
        m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
    }
};

/** \ingroup CholmodSupport_Module
  * \class CholmodSupernodalLLT
  * \brief A supernodal Cholesky (LLT) factorization and solver based on Cholmod
  *
  * This class allows to solve for A.X = B sparse linear problems via a supernodal LL^T Cholesky factorization
  * using the Cholmod library.
  * This supernodal variant performs best on dense enough problems, e.g., 3D FEM, or very high order 2D FEM.
  * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
  * X and B can be either dense or sparse.
  *
  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
  *               or Upper. Default is Lower.
  *
  * \implsparsesolverconcept
  *
  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
  *
  * \warning Only double precision real and complex scalar types are supported by Cholmod.
  *
  * \sa \ref TutorialSparseSolverConcept
  */
template <typename _MatrixType, int _UpLo = Lower> class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT<_MatrixType, _UpLo>>
{
    typedef CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT> Base;
    using Base::m_cholmod;

public:
    typedef _MatrixType MatrixType;

    CholmodSupernodalLLT() : Base() { init(); }

    CholmodSupernodalLLT(const MatrixType& matrix) : Base()
    {
        init();
        this->compute(matrix);
    }

    ~CholmodSupernodalLLT() {}

protected:
    void init()
    {
        m_cholmod.final_asis = 1;
        m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
    }
};

/** \ingroup CholmodSupport_Module
  * \class CholmodDecomposition
  * \brief A general Cholesky factorization and solver based on Cholmod
  *
  * This class allows to solve for A.X = B sparse linear problems via a LL^T or LDL^T Cholesky factorization
  * using the Cholmod library. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
  * X and B can be either dense or sparse.
  *
  * This variant permits to change the underlying Cholesky method at runtime.
  * On the other hand, it does not provide access to the result of the factorization.
  * The default is to let Cholmod automatically choose between a simplicial and supernodal factorization.
  *
  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
  *               or Upper. Default is Lower.
  *
  * \implsparsesolverconcept
  *
  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
  *
  * \warning Only double precision real and complex scalar types are supported by Cholmod.
  *
  * \sa \ref TutorialSparseSolverConcept
  */
template <typename _MatrixType, int _UpLo = Lower> class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecomposition<_MatrixType, _UpLo>>
{
    typedef CholmodBase<_MatrixType, _UpLo, CholmodDecomposition> Base;
    using Base::m_cholmod;

public:
    typedef _MatrixType MatrixType;

    CholmodDecomposition() : Base() { init(); }

    CholmodDecomposition(const MatrixType& matrix) : Base()
    {
        init();
        this->compute(matrix);
    }

    ~CholmodDecomposition() {}

    void setMode(CholmodMode mode)
    {
        switch (mode)
        {
        case CholmodAuto:
            m_cholmod.final_asis = 1;
            m_cholmod.supernodal = CHOLMOD_AUTO;
            break;
        case CholmodSimplicialLLt:
            m_cholmod.final_asis = 0;
            m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
            m_cholmod.final_ll = 1;
            break;
        case CholmodSupernodalLLt:
            m_cholmod.final_asis = 1;
            m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
            break;
        case CholmodLDLt:
            m_cholmod.final_asis = 1;
            m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
            break;
        default:
            break;
        }
    }

protected:
    void init()
    {
        m_cholmod.final_asis = 1;
        m_cholmod.supernodal = CHOLMOD_AUTO;
    }
};

}  // end namespace Eigen

#endif  // EIGEN_CHOLMODSUPPORT_H
